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About 8% of the Ecuadorian population suffers some type of amputation of upper or lower limbs. Due to the high cost of a prosthesis and the fact that the salary of an average worker in the country reached 248 USD in August 2021, they experience a great labor disadvantage and only 17% of them are employed. Thanks to advances in 3D printing and the accessibility of bioelectric sensors, it is now possible to create economically accessible proposals. This work proposes the design of a hand prosthesis that uses electromyography (EMG) signals and neural networks for real-time control. The integrated system has a mechanical and electronic design, and the latter integrates artificial intelligence for control. To train the algorithm, an experimental methodology was developed to record muscle activity in upper extremities associated with specific tasks, using three EMG surface sensors. These data were used to train a five-layer neural network. the trained model was compressed and exported using TensorflowLite. The prosthesis consisted of a gripper and a pivot base, which were designed in Fusion 360 considering the movement restrictions and the maximum loads. It was actuated in real time thanks to the design of an electronic circuit that used an ESP32 development board, which was responsible for recording, processing and classifying the EMG signals associated with a motor intention, and to actuate the hand prosthesis. As a result of this work, a database with 60 electromyographic activity records from three tasks was released. The classification algorithm was able to detect the three muscle tasks with an accuracy of 78.67% and a response time of 80 ms. Finally, the 3D printed prosthesis was able to support a weight of 500 g with a safety factor equal to 15.

Gait studies in the elderly population have been always conducted in gait labs or spacious clinical facilities, which influence gait parameters, and also implies that the participants have to be able to move to these facilities. Indoors gait characteristics of the elderly population have been very little studied. In this study, we aim to define the normal limits of the spatial gait parameters of the elderly, when walking at home, and to analyze relationship existing between the spatial gait parameters to other health variables. For such purpose, we conducted a transversal study on a probabilistic sample of 431 Spanish community-dwelling older, in which the spatial gait parameters were recorded by using an ink footprints method. We found that the mean stride length indoors was 88.47 cm (SD:26.05 cm; mean CI95%:85.52-91.41 cm), and the mean step width was 10.34 cm (SD:4.37 cm; mean CI95%:9.84-10.83 cm). Stride length was shorter in women and the oldest group, and was significantly influenced by the strength, balance, and physical activity. Stride width was larger in the oldest group and mainly affected by balance. A composite parameter including width and normalized stride length was independent from sex, and strongly differentiated between age groups. This parameter was affected by strength.